Overview

Brought to you by YData

Dataset statistics

Number of variables32
Number of observations66448
Missing cells73318
Missing cells (%)3.4%
Duplicate rows4394
Duplicate rows (%)6.6%
Total size in memory16.2 MiB
Average record size in memory256.0 B

Variable types

Categorical16
Numeric14
Text1
DateTime1

Alerts

Dataset has 4394 (6.6%) duplicate rowsDuplicates
agent is highly overall correlated with company and 1 other fieldsHigh correlation
arrival_date_month is highly overall correlated with arrival_date_week_numberHigh correlation
arrival_date_week_number is highly overall correlated with arrival_date_monthHigh correlation
assigned_room_type is highly overall correlated with reserved_room_typeHigh correlation
company is highly overall correlated with agentHigh correlation
distribution_channel is highly overall correlated with market_segmentHigh correlation
hotel is highly overall correlated with agentHigh correlation
is_canceled is highly overall correlated with reservation_statusHigh correlation
market_segment is highly overall correlated with distribution_channelHigh correlation
reservation_status is highly overall correlated with is_canceledHigh correlation
reserved_room_type is highly overall correlated with assigned_room_typeHigh correlation
children is highly imbalanced (80.0%) Imbalance
babies is highly imbalanced (96.1%) Imbalance
meal is highly imbalanced (53.1%) Imbalance
distribution_channel is highly imbalanced (61.5%) Imbalance
is_repeated_guest is highly imbalanced (82.2%) Imbalance
reserved_room_type is highly imbalanced (53.0%) Imbalance
deposit_type is highly imbalanced (62.5%) Imbalance
customer_type is highly imbalanced (50.3%) Imbalance
required_car_parking_spaces is highly imbalanced (81.7%) Imbalance
agent has 10124 (15.2%) missing values Missing
company has 62703 (94.4%) missing values Missing
adults is highly skewed (γ1 = 27.11728186) Skewed
previous_cancellations is highly skewed (γ1 = 22.47111231) Skewed
lead_time has 3749 (5.6%) zeros Zeros
stays_in_weekend_nights has 27125 (40.8%) zeros Zeros
stays_in_week_nights has 3977 (6.0%) zeros Zeros
previous_cancellations has 65353 (98.4%) zeros Zeros
previous_bookings_not_canceled has 64416 (96.9%) zeros Zeros
booking_changes has 56399 (84.9%) zeros Zeros
days_in_waiting_list has 63984 (96.3%) zeros Zeros
adr has 975 (1.5%) zeros Zeros
total_of_special_requests has 42616 (64.1%) zeros Zeros

Reproduction

Analysis started2024-11-12 16:08:41.691013
Analysis finished2024-11-12 16:09:55.723122
Duration1 minute and 14.03 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

hotel
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size519.2 KiB
Resort Hotel
40060 
City Hotel
26388 

Length

Max length12
Median length12
Mean length11.205755
Min length10

Characters and Unicode

Total characters744600
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowResort Hotel
2nd rowResort Hotel
3rd rowResort Hotel
4th rowResort Hotel
5th rowResort Hotel

Common Values

ValueCountFrequency (%)
Resort Hotel 40060
60.3%
City Hotel 26388
39.7%

Length

2024-11-12T16:09:55.954182image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-12T16:09:56.222289image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
hotel 66448
50.0%
resort 40060
30.1%
city 26388
 
19.9%

Most occurring characters

ValueCountFrequency (%)
t 132896
17.8%
e 106508
14.3%
o 106508
14.3%
66448
8.9%
H 66448
8.9%
l 66448
8.9%
R 40060
 
5.4%
s 40060
 
5.4%
r 40060
 
5.4%
C 26388
 
3.5%
Other values (2) 52776
 
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 744600
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 132896
17.8%
e 106508
14.3%
o 106508
14.3%
66448
8.9%
H 66448
8.9%
l 66448
8.9%
R 40060
 
5.4%
s 40060
 
5.4%
r 40060
 
5.4%
C 26388
 
3.5%
Other values (2) 52776
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 744600
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 132896
17.8%
e 106508
14.3%
o 106508
14.3%
66448
8.9%
H 66448
8.9%
l 66448
8.9%
R 40060
 
5.4%
s 40060
 
5.4%
r 40060
 
5.4%
C 26388
 
3.5%
Other values (2) 52776
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 744600
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 132896
17.8%
e 106508
14.3%
o 106508
14.3%
66448
8.9%
H 66448
8.9%
l 66448
8.9%
R 40060
 
5.4%
s 40060
 
5.4%
r 40060
 
5.4%
C 26388
 
3.5%
Other values (2) 52776
 
7.1%

is_canceled
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size519.2 KiB
0
34681 
1
31767 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters66448
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 34681
52.2%
1 31767
47.8%

Length

2024-11-12T16:09:56.509158image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-12T16:09:56.735036image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 34681
52.2%
1 31767
47.8%

Most occurring characters

ValueCountFrequency (%)
0 34681
52.2%
1 31767
47.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 66448
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 34681
52.2%
1 31767
47.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 66448
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 34681
52.2%
1 31767
47.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 66448
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 34681
52.2%
1 31767
47.8%

lead_time
Real number (ℝ)

Zeros 

Distinct454
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.29903
Minimum0
Maximum737
Zeros3749
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size519.2 KiB
2024-11-12T16:09:57.000167image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q119
median70
Q3160
95-th percentile316
Maximum737
Range737
Interquartile range (IQR)141

Descriptive statistics

Standard deviation107.47458
Coefficient of variation (CV)1.0304466
Kurtosis2.2741708
Mean104.29903
Median Absolute Deviation (MAD)60
Skewness1.4451424
Sum6930462
Variance11550.785
MonotonicityNot monotonic
2024-11-12T16:09:57.358407image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3749
 
5.6%
1 1948
 
2.9%
2 1138
 
1.7%
3 967
 
1.5%
4 855
 
1.3%
5 760
 
1.1%
7 717
 
1.1%
6 711
 
1.1%
12 578
 
0.9%
10 570
 
0.9%
Other values (444) 54455
82.0%
ValueCountFrequency (%)
0 3749
5.6%
1 1948
2.9%
2 1138
 
1.7%
3 967
 
1.5%
4 855
 
1.3%
5 760
 
1.1%
6 711
 
1.1%
7 717
 
1.1%
8 544
 
0.8%
9 527
 
0.8%
ValueCountFrequency (%)
737 1
 
< 0.1%
709 1
 
< 0.1%
629 17
< 0.1%
626 30
< 0.1%
622 17
< 0.1%
615 17
< 0.1%
608 17
< 0.1%
605 30
< 0.1%
601 17
< 0.1%
594 17
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size519.2 KiB
2016
34210 
2017
17563 
2015
14675 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters265792
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015
2nd row2015
3rd row2015
4th row2015
5th row2015

Common Values

ValueCountFrequency (%)
2016 34210
51.5%
2017 17563
26.4%
2015 14675
22.1%

Length

2024-11-12T16:09:57.675643image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-12T16:09:57.912065image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
2016 34210
51.5%
2017 17563
26.4%
2015 14675
22.1%

Most occurring characters

ValueCountFrequency (%)
2 66448
25.0%
0 66448
25.0%
1 66448
25.0%
6 34210
12.9%
7 17563
 
6.6%
5 14675
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 265792
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 66448
25.0%
0 66448
25.0%
1 66448
25.0%
6 34210
12.9%
7 17563
 
6.6%
5 14675
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 265792
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 66448
25.0%
0 66448
25.0%
1 66448
25.0%
6 34210
12.9%
7 17563
 
6.6%
5 14675
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 265792
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 66448
25.0%
0 66448
25.0%
1 66448
25.0%
6 34210
12.9%
7 17563
 
6.6%
5 14675
 
5.5%

arrival_date_month
Categorical

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size519.2 KiB
August
7714 
October
6842 
September
6712 
April
6284 
July
6176 
Other values (7)
32720 

Length

Max length9
Median length7
Mean length6.110974
Min length3

Characters and Unicode

Total characters406062
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJuly
2nd rowJuly
3rd rowJuly
4th rowJuly
5th rowJuly

Common Values

ValueCountFrequency (%)
August 7714
11.6%
October 6842
10.3%
September 6712
10.1%
April 6284
9.5%
July 6176
9.3%
March 5766
8.7%
May 5282
7.9%
February 4798
7.2%
June 4725
7.1%
November 4204
6.3%
Other values (2) 7945
12.0%

Length

2024-11-12T16:09:58.195677image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
august 7714
11.6%
october 6842
10.3%
september 6712
10.1%
april 6284
9.5%
july 6176
9.3%
march 5766
8.7%
may 5282
7.9%
february 4798
7.2%
june 4725
7.1%
november 4204
6.3%
Other values (2) 7945
12.0%

Most occurring characters

ValueCountFrequency (%)
e 57344
14.1%
r 47349
 
11.7%
u 34927
 
8.6%
b 26701
 
6.6%
a 23446
 
5.8%
t 21268
 
5.2%
y 20056
 
4.9%
c 16753
 
4.1%
m 15061
 
3.7%
J 14701
 
3.6%
Other values (16) 128456
31.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 406062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 57344
14.1%
r 47349
 
11.7%
u 34927
 
8.6%
b 26701
 
6.6%
a 23446
 
5.8%
t 21268
 
5.2%
y 20056
 
4.9%
c 16753
 
4.1%
m 15061
 
3.7%
J 14701
 
3.6%
Other values (16) 128456
31.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 406062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 57344
14.1%
r 47349
 
11.7%
u 34927
 
8.6%
b 26701
 
6.6%
a 23446
 
5.8%
t 21268
 
5.2%
y 20056
 
4.9%
c 16753
 
4.1%
m 15061
 
3.7%
J 14701
 
3.6%
Other values (16) 128456
31.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 406062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 57344
14.1%
r 47349
 
11.7%
u 34927
 
8.6%
b 26701
 
6.6%
a 23446
 
5.8%
t 21268
 
5.2%
y 20056
 
4.9%
c 16753
 
4.1%
m 15061
 
3.7%
J 14701
 
3.6%
Other values (16) 128456
31.6%

arrival_date_week_number
Real number (ℝ)

High correlation 

Distinct53
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.524696
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.2 KiB
2024-11-12T16:09:58.512003image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q115
median29
Q339
95-th percentile49
Maximum53
Range52
Interquartile range (IQR)24

Descriptive statistics

Standard deviation14.175895
Coefficient of variation (CV)0.51502458
Kurtosis-1.1018194
Mean27.524696
Median Absolute Deviation (MAD)12
Skewness-0.081017607
Sum1828961
Variance200.956
MonotonicityNot monotonic
2024-11-12T16:09:58.852990image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33 2023
 
3.0%
15 1711
 
2.6%
34 1703
 
2.6%
41 1667
 
2.5%
38 1663
 
2.5%
32 1619
 
2.4%
42 1617
 
2.4%
37 1555
 
2.3%
40 1513
 
2.3%
43 1505
 
2.3%
Other values (43) 49872
75.1%
ValueCountFrequency (%)
1 625
0.9%
2 852
1.3%
3 888
1.3%
4 978
1.5%
5 801
1.2%
6 950
1.4%
7 1368
2.1%
8 1157
1.7%
9 1251
1.9%
10 1224
1.8%
ValueCountFrequency (%)
53 1130
1.7%
52 800
1.2%
51 617
0.9%
50 760
1.1%
49 1099
1.7%
48 900
1.4%
47 1047
1.6%
46 930
1.4%
45 1281
1.9%
44 1372
2.1%

arrival_date_day_of_month
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.670675
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.2 KiB
2024-11-12T16:09:59.156210image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8050254
Coefficient of variation (CV)0.56187915
Kurtosis-1.1807551
Mean15.670675
Median Absolute Deviation (MAD)8
Skewness0.026508746
Sum1041285
Variance77.528472
MonotonicityNot monotonic
2024-11-12T16:09:59.467467image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
5 2506
 
3.8%
12 2474
 
3.7%
16 2464
 
3.7%
17 2372
 
3.6%
18 2357
 
3.5%
25 2342
 
3.5%
2 2341
 
3.5%
9 2340
 
3.5%
15 2334
 
3.5%
30 2306
 
3.5%
Other values (21) 42612
64.1%
ValueCountFrequency (%)
1 2090
3.1%
2 2341
3.5%
3 2160
3.3%
4 2120
3.2%
5 2506
3.8%
6 2032
3.1%
7 2196
3.3%
8 2101
3.2%
9 2340
3.5%
10 1903
2.9%
ValueCountFrequency (%)
31 1302
2.0%
30 2306
3.5%
29 1891
2.8%
28 2101
3.2%
27 1924
2.9%
26 2306
3.5%
25 2342
3.5%
24 2133
3.2%
23 2034
3.1%
22 1964
3.0%

stays_in_weekend_nights
Real number (ℝ)

Zeros 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0312425
Minimum0
Maximum19
Zeros27125
Zeros (%)40.8%
Negative0
Negative (%)0.0%
Memory size519.2 KiB
2024-11-12T16:09:59.730715image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile2
Maximum19
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.083307
Coefficient of variation (CV)1.0504871
Kurtosis7.465002
Mean1.0312425
Median Absolute Deviation (MAD)1
Skewness1.4652904
Sum68524
Variance1.173554
MonotonicityNot monotonic
2024-11-12T16:09:59.999640image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 27125
40.8%
2 20216
30.4%
1 16031
24.1%
4 1706
 
2.6%
3 1066
 
1.6%
6 143
 
0.2%
5 61
 
0.1%
8 54
 
0.1%
7 19
 
< 0.1%
9 8
 
< 0.1%
Other values (7) 19
 
< 0.1%
ValueCountFrequency (%)
0 27125
40.8%
1 16031
24.1%
2 20216
30.4%
3 1066
 
1.6%
4 1706
 
2.6%
5 61
 
0.1%
6 143
 
0.2%
7 19
 
< 0.1%
8 54
 
0.1%
9 8
 
< 0.1%
ValueCountFrequency (%)
19 1
 
< 0.1%
18 1
 
< 0.1%
16 2
 
< 0.1%
14 1
 
< 0.1%
13 2
 
< 0.1%
12 5
 
< 0.1%
10 7
 
< 0.1%
9 8
 
< 0.1%
8 54
0.1%
7 19
 
< 0.1%

stays_in_week_nights
Real number (ℝ)

Zeros 

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8015892
Minimum0
Maximum50
Zeros3977
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size519.2 KiB
2024-11-12T16:10:00.301334image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile6
Maximum50
Range50
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.1883284
Coefficient of variation (CV)0.78110253
Kurtosis19.55695
Mean2.8015892
Median Absolute Deviation (MAD)1
Skewness2.6643508
Sum186160
Variance4.7887813
MonotonicityNot monotonic
2024-11-12T16:10:00.628852image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
2 17216
25.9%
1 15092
22.7%
3 11575
17.4%
5 8853
13.3%
4 5469
 
8.2%
0 3977
 
6.0%
6 1273
 
1.9%
10 966
 
1.5%
7 918
 
1.4%
8 551
 
0.8%
Other values (23) 558
 
0.8%
ValueCountFrequency (%)
0 3977
 
6.0%
1 15092
22.7%
2 17216
25.9%
3 11575
17.4%
4 5469
 
8.2%
5 8853
13.3%
6 1273
 
1.9%
7 918
 
1.4%
8 551
 
0.8%
9 195
 
0.3%
ValueCountFrequency (%)
50 1
 
< 0.1%
42 1
 
< 0.1%
40 2
 
< 0.1%
34 1
 
< 0.1%
33 1
 
< 0.1%
32 1
 
< 0.1%
30 4
< 0.1%
26 1
 
< 0.1%
25 5
< 0.1%
24 1
 
< 0.1%

adults
Real number (ℝ)

Skewed 

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8648116
Minimum0
Maximum55
Zeros142
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size519.2 KiB
2024-11-12T16:10:00.908005image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q32
95-th percentile2
Maximum55
Range55
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.61908148
Coefficient of variation (CV)0.33198072
Kurtosis1860.2495
Mean1.8648116
Median Absolute Deviation (MAD)0
Skewness27.117282
Sum123913
Variance0.38326188
MonotonicityNot monotonic
2024-11-12T16:10:01.170689image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2 51718
77.8%
1 11835
 
17.8%
3 2701
 
4.1%
0 142
 
0.2%
4 36
 
0.1%
26 5
 
< 0.1%
27 2
 
< 0.1%
20 2
 
< 0.1%
5 2
 
< 0.1%
40 1
 
< 0.1%
Other values (4) 4
 
< 0.1%
ValueCountFrequency (%)
0 142
 
0.2%
1 11835
 
17.8%
2 51718
77.8%
3 2701
 
4.1%
4 36
 
0.1%
5 2
 
< 0.1%
6 1
 
< 0.1%
10 1
 
< 0.1%
20 2
 
< 0.1%
26 5
 
< 0.1%
ValueCountFrequency (%)
55 1
 
< 0.1%
50 1
 
< 0.1%
40 1
 
< 0.1%
27 2
 
< 0.1%
26 5
 
< 0.1%
20 2
 
< 0.1%
10 1
 
< 0.1%
6 1
 
< 0.1%
5 2
 
< 0.1%
4 36
0.1%

children
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing4
Missing (%)< 0.1%
Memory size519.2 KiB
0.0
61446 
1.0
 
2637
2.0
 
2330
3.0
 
30
10.0
 
1

Length

Max length4
Median length3
Mean length3.0000151
Min length3

Characters and Unicode

Total characters199333
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 61446
92.5%
1.0 2637
 
4.0%
2.0 2330
 
3.5%
3.0 30
 
< 0.1%
10.0 1
 
< 0.1%
(Missing) 4
 
< 0.1%

Length

2024-11-12T16:10:01.487081image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-12T16:10:01.778879image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 61446
92.5%
1.0 2637
 
4.0%
2.0 2330
 
3.5%
3.0 30
 
< 0.1%
10.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 127891
64.2%
. 66444
33.3%
1 2638
 
1.3%
2 2330
 
1.2%
3 30
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 199333
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 127891
64.2%
. 66444
33.3%
1 2638
 
1.3%
2 2330
 
1.2%
3 30
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 199333
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 127891
64.2%
. 66444
33.3%
1 2638
 
1.3%
2 2330
 
1.2%
3 30
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 199333
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 127891
64.2%
. 66444
33.3%
1 2638
 
1.3%
2 2330
 
1.2%
3 30
 
< 0.1%

babies
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size519.2 KiB
0
65824 
1
 
614
2
 
9
10
 
1

Length

Max length2
Median length1
Mean length1.000015
Min length1

Characters and Unicode

Total characters66449
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 65824
99.1%
1 614
 
0.9%
2 9
 
< 0.1%
10 1
 
< 0.1%

Length

2024-11-12T16:10:02.117941image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-12T16:10:02.408172image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 65824
99.1%
1 614
 
0.9%
2 9
 
< 0.1%
10 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 65825
99.1%
1 615
 
0.9%
2 9
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 66449
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 65825
99.1%
1 615
 
0.9%
2 9
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 66449
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 65825
99.1%
1 615
 
0.9%
2 9
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 66449
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 65825
99.1%
1 615
 
0.9%
2 9
 
< 0.1%

meal
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size519.2 KiB
BB
51129 
HB
10335 
SC
 
3025
Undefined
 
1169
FB
 
790

Length

Max length9
Median length2
Mean length2.1231489
Min length2

Characters and Unicode

Total characters141079
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBB
2nd rowBB
3rd rowBB
4th rowBB
5th rowBB

Common Values

ValueCountFrequency (%)
BB 51129
76.9%
HB 10335
 
15.6%
SC 3025
 
4.6%
Undefined 1169
 
1.8%
FB 790
 
1.2%

Length

2024-11-12T16:10:02.738629image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-12T16:10:03.038028image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
bb 51129
76.9%
hb 10335
 
15.6%
sc 3025
 
4.6%
undefined 1169
 
1.8%
fb 790
 
1.2%

Most occurring characters

ValueCountFrequency (%)
B 113383
80.4%
H 10335
 
7.3%
S 3025
 
2.1%
C 3025
 
2.1%
n 2338
 
1.7%
d 2338
 
1.7%
e 2338
 
1.7%
U 1169
 
0.8%
f 1169
 
0.8%
i 1169
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 141079
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 113383
80.4%
H 10335
 
7.3%
S 3025
 
2.1%
C 3025
 
2.1%
n 2338
 
1.7%
d 2338
 
1.7%
e 2338
 
1.7%
U 1169
 
0.8%
f 1169
 
0.8%
i 1169
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 141079
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 113383
80.4%
H 10335
 
7.3%
S 3025
 
2.1%
C 3025
 
2.1%
n 2338
 
1.7%
d 2338
 
1.7%
e 2338
 
1.7%
U 1169
 
0.8%
f 1169
 
0.8%
i 1169
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 141079
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 113383
80.4%
H 10335
 
7.3%
S 3025
 
2.1%
C 3025
 
2.1%
n 2338
 
1.7%
d 2338
 
1.7%
e 2338
 
1.7%
U 1169
 
0.8%
f 1169
 
0.8%
i 1169
 
0.8%
Distinct147
Distinct (%)0.2%
Missing486
Missing (%)0.7%
Memory size519.2 KiB
2024-11-12T16:10:03.445378image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.987326
Min length2

Characters and Unicode

Total characters197050
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)< 0.1%

Sample

1st rowPRT
2nd rowPRT
3rd rowGBR
4th rowGBR
5th rowGBR
ValueCountFrequency (%)
prt 31276
47.4%
gbr 7980
 
12.1%
esp 5681
 
8.6%
fra 3514
 
5.3%
irl 2464
 
3.7%
deu 2370
 
3.6%
ita 1689
 
2.6%
bra 1019
 
1.5%
nld 844
 
1.3%
cn 836
 
1.3%
Other values (137) 8289
 
12.6%
2024-11-12T16:10:04.408594image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 48057
24.4%
P 37614
19.1%
T 33794
17.1%
E 10338
 
5.2%
B 9932
 
5.0%
G 8512
 
4.3%
A 8423
 
4.3%
S 7907
 
4.0%
L 5073
 
2.6%
U 5021
 
2.5%
Other values (16) 22379
11.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 197050
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 48057
24.4%
P 37614
19.1%
T 33794
17.1%
E 10338
 
5.2%
B 9932
 
5.0%
G 8512
 
4.3%
A 8423
 
4.3%
S 7907
 
4.0%
L 5073
 
2.6%
U 5021
 
2.5%
Other values (16) 22379
11.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 197050
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 48057
24.4%
P 37614
19.1%
T 33794
17.1%
E 10338
 
5.2%
B 9932
 
5.0%
G 8512
 
4.3%
A 8423
 
4.3%
S 7907
 
4.0%
L 5073
 
2.6%
U 5021
 
2.5%
Other values (16) 22379
11.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 197050
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 48057
24.4%
P 37614
19.1%
T 33794
17.1%
E 10338
 
5.2%
B 9932
 
5.0%
G 8512
 
4.3%
A 8423
 
4.3%
S 7907
 
4.0%
L 5073
 
2.6%
U 5021
 
2.5%
Other values (16) 22379
11.4%

market_segment
Categorical

High correlation 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size519.2 KiB
Online TA
29901 
Offline TA/TO
13419 
Groups
12381 
Direct
7638 
Corporate
 
2804
Other values (3)
 
305

Length

Max length13
Median length9
Mean length8.919802
Min length6

Characters and Unicode

Total characters592703
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDirect
2nd rowDirect
3rd rowDirect
4th rowCorporate
5th rowOnline TA

Common Values

ValueCountFrequency (%)
Online TA 29901
45.0%
Offline TA/TO 13419
20.2%
Groups 12381
18.6%
Direct 7638
 
11.5%
Corporate 2804
 
4.2%
Complementary 271
 
0.4%
Aviation 32
 
< 0.1%
Undefined 2
 
< 0.1%

Length

2024-11-12T16:10:05.076013image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-12T16:10:05.622763image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
online 29901
27.2%
ta 29901
27.2%
offline 13419
12.2%
ta/to 13419
12.2%
groups 12381
11.3%
direct 7638
 
7.0%
corporate 2804
 
2.6%
complementary 271
 
0.2%
aviation 32
 
< 0.1%
undefined 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 73528
12.4%
O 56739
9.6%
T 56739
9.6%
e 54308
9.2%
i 51024
8.6%
l 43591
 
7.4%
A 43352
 
7.3%
43320
 
7.3%
f 26840
 
4.5%
r 25898
 
4.4%
Other values (16) 117364
19.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 592703
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 73528
12.4%
O 56739
9.6%
T 56739
9.6%
e 54308
9.2%
i 51024
8.6%
l 43591
 
7.4%
A 43352
 
7.3%
43320
 
7.3%
f 26840
 
4.5%
r 25898
 
4.4%
Other values (16) 117364
19.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 592703
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 73528
12.4%
O 56739
9.6%
T 56739
9.6%
e 54308
9.2%
i 51024
8.6%
l 43591
 
7.4%
A 43352
 
7.3%
43320
 
7.3%
f 26840
 
4.5%
r 25898
 
4.4%
Other values (16) 117364
19.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 592703
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 73528
12.4%
O 56739
9.6%
T 56739
9.6%
e 54308
9.2%
i 51024
8.6%
l 43591
 
7.4%
A 43352
 
7.3%
43320
 
7.3%
f 26840
 
4.5%
r 25898
 
4.4%
Other values (16) 117364
19.8%

distribution_channel
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size519.2 KiB
TA/TO
53383 
Direct
9148 
Corporate
 
3882
GDS
 
30
Undefined
 
5

Length

Max length9
Median length5
Mean length5.3707561
Min length3

Characters and Unicode

Total characters356876
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDirect
2nd rowDirect
3rd rowDirect
4th rowCorporate
5th rowTA/TO

Common Values

ValueCountFrequency (%)
TA/TO 53383
80.3%
Direct 9148
 
13.8%
Corporate 3882
 
5.8%
GDS 30
 
< 0.1%
Undefined 5
 
< 0.1%

Length

2024-11-12T16:10:06.215109image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-12T16:10:06.688772image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
ta/to 53383
80.3%
direct 9148
 
13.8%
corporate 3882
 
5.8%
gds 30
 
< 0.1%
undefined 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
T 106766
29.9%
/ 53383
15.0%
O 53383
15.0%
A 53383
15.0%
r 16912
 
4.7%
e 13040
 
3.7%
t 13030
 
3.7%
D 9178
 
2.6%
i 9153
 
2.6%
c 9148
 
2.6%
Other values (10) 19500
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 356876
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 106766
29.9%
/ 53383
15.0%
O 53383
15.0%
A 53383
15.0%
r 16912
 
4.7%
e 13040
 
3.7%
t 13030
 
3.7%
D 9178
 
2.6%
i 9153
 
2.6%
c 9148
 
2.6%
Other values (10) 19500
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 356876
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 106766
29.9%
/ 53383
15.0%
O 53383
15.0%
A 53383
15.0%
r 16912
 
4.7%
e 13040
 
3.7%
t 13030
 
3.7%
D 9178
 
2.6%
i 9153
 
2.6%
c 9148
 
2.6%
Other values (10) 19500
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 356876
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 106766
29.9%
/ 53383
15.0%
O 53383
15.0%
A 53383
15.0%
r 16912
 
4.7%
e 13040
 
3.7%
t 13030
 
3.7%
D 9178
 
2.6%
i 9153
 
2.6%
c 9148
 
2.6%
Other values (10) 19500
 
5.5%

is_repeated_guest
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size519.2 KiB
0
64670 
1
 
1778

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters66448
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 64670
97.3%
1 1778
 
2.7%

Length

2024-11-12T16:10:07.291173image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-12T16:10:07.774040image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 64670
97.3%
1 1778
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 64670
97.3%
1 1778
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 66448
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 64670
97.3%
1 1778
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 66448
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 64670
97.3%
1 1778
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 66448
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 64670
97.3%
1 1778
 
2.7%

previous_cancellations
Real number (ℝ)

Skewed  Zeros 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06132615
Minimum0
Maximum26
Zeros65353
Zeros (%)98.4%
Negative0
Negative (%)0.0%
Memory size519.2 KiB
2024-11-12T16:10:07.990675image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum26
Range26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0378418
Coefficient of variation (CV)16.923315
Kurtosis518.27002
Mean0.06132615
Median Absolute Deviation (MAD)0
Skewness22.471112
Sum4075
Variance1.0771155
MonotonicityNot monotonic
2024-11-12T16:10:08.253866image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 65353
98.4%
1 896
 
1.3%
24 48
 
0.1%
2 44
 
0.1%
26 26
 
< 0.1%
25 25
 
< 0.1%
19 19
 
< 0.1%
3 14
 
< 0.1%
14 14
 
< 0.1%
4 6
 
< 0.1%
ValueCountFrequency (%)
0 65353
98.4%
1 896
 
1.3%
2 44
 
0.1%
3 14
 
< 0.1%
4 6
 
< 0.1%
5 3
 
< 0.1%
14 14
 
< 0.1%
19 19
 
< 0.1%
24 48
 
0.1%
25 25
 
< 0.1%
ValueCountFrequency (%)
26 26
 
< 0.1%
25 25
 
< 0.1%
24 48
 
0.1%
19 19
 
< 0.1%
14 14
 
< 0.1%
5 3
 
< 0.1%
4 6
 
< 0.1%
3 14
 
< 0.1%
2 44
 
0.1%
1 896
1.3%

previous_bookings_not_canceled
Real number (ℝ)

Zeros 

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.088294606
Minimum0
Maximum30
Zeros64416
Zeros (%)96.9%
Negative0
Negative (%)0.0%
Memory size519.2 KiB
2024-11-12T16:10:08.530296image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum30
Range30
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7812591
Coefficient of variation (CV)8.848322
Kurtosis405.48154
Mean0.088294606
Median Absolute Deviation (MAD)0
Skewness17.059997
Sum5867
Variance0.61036579
MonotonicityNot monotonic
2024-11-12T16:10:08.850772image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 64416
96.9%
1 973
 
1.5%
2 388
 
0.6%
3 204
 
0.3%
4 127
 
0.2%
5 91
 
0.1%
6 56
 
0.1%
7 37
 
0.1%
8 33
 
< 0.1%
9 24
 
< 0.1%
Other values (21) 99
 
0.1%
ValueCountFrequency (%)
0 64416
96.9%
1 973
 
1.5%
2 388
 
0.6%
3 204
 
0.3%
4 127
 
0.2%
5 91
 
0.1%
6 56
 
0.1%
7 37
 
0.1%
8 33
 
< 0.1%
9 24
 
< 0.1%
ValueCountFrequency (%)
30 1
 
< 0.1%
29 1
 
< 0.1%
28 1
 
< 0.1%
27 2
< 0.1%
26 1
 
< 0.1%
25 3
< 0.1%
24 2
< 0.1%
23 2
< 0.1%
22 2
< 0.1%
21 2
< 0.1%

reserved_room_type
Categorical

High correlation  Imbalance 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size519.2 KiB
A
45190 
D
10588 
E
5283 
F
 
1701
G
 
1678
Other values (5)
 
2008

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters66448
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowC
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 45190
68.0%
D 10588
 
15.9%
E 5283
 
8.0%
F 1701
 
2.6%
G 1678
 
2.5%
C 922
 
1.4%
H 601
 
0.9%
B 469
 
0.7%
P 10
 
< 0.1%
L 6
 
< 0.1%

Length

2024-11-12T16:10:09.179995image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-12T16:10:09.469029image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
a 45190
68.0%
d 10588
 
15.9%
e 5283
 
8.0%
f 1701
 
2.6%
g 1678
 
2.5%
c 922
 
1.4%
h 601
 
0.9%
b 469
 
0.7%
p 10
 
< 0.1%
l 6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 45190
68.0%
D 10588
 
15.9%
E 5283
 
8.0%
F 1701
 
2.6%
G 1678
 
2.5%
C 922
 
1.4%
H 601
 
0.9%
B 469
 
0.7%
P 10
 
< 0.1%
L 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 66448
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 45190
68.0%
D 10588
 
15.9%
E 5283
 
8.0%
F 1701
 
2.6%
G 1678
 
2.5%
C 922
 
1.4%
H 601
 
0.9%
B 469
 
0.7%
P 10
 
< 0.1%
L 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 66448
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 45190
68.0%
D 10588
 
15.9%
E 5283
 
8.0%
F 1701
 
2.6%
G 1678
 
2.5%
C 922
 
1.4%
H 601
 
0.9%
B 469
 
0.7%
P 10
 
< 0.1%
L 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 66448
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 45190
68.0%
D 10588
 
15.9%
E 5283
 
8.0%
F 1701
 
2.6%
G 1678
 
2.5%
C 922
 
1.4%
H 601
 
0.9%
B 469
 
0.7%
P 10
 
< 0.1%
L 6
 
< 0.1%

assigned_room_type
Categorical

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size519.2 KiB
A
37359 
D
14447 
E
6076 
F
 
2362
C
 
2229
Other values (7)
3975 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters66448
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowC
2nd rowC
3rd rowC
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 37359
56.2%
D 14447
 
21.7%
E 6076
 
9.1%
F 2362
 
3.6%
C 2229
 
3.4%
G 1951
 
2.9%
B 912
 
1.4%
H 712
 
1.1%
I 363
 
0.5%
K 26
 
< 0.1%
Other values (2) 11
 
< 0.1%

Length

2024-11-12T16:10:09.798429image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a 37359
56.2%
d 14447
 
21.7%
e 6076
 
9.1%
f 2362
 
3.6%
c 2229
 
3.4%
g 1951
 
2.9%
b 912
 
1.4%
h 712
 
1.1%
i 363
 
0.5%
k 26
 
< 0.1%
Other values (2) 11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 37359
56.2%
D 14447
 
21.7%
E 6076
 
9.1%
F 2362
 
3.6%
C 2229
 
3.4%
G 1951
 
2.9%
B 912
 
1.4%
H 712
 
1.1%
I 363
 
0.5%
K 26
 
< 0.1%
Other values (2) 11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 66448
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 37359
56.2%
D 14447
 
21.7%
E 6076
 
9.1%
F 2362
 
3.6%
C 2229
 
3.4%
G 1951
 
2.9%
B 912
 
1.4%
H 712
 
1.1%
I 363
 
0.5%
K 26
 
< 0.1%
Other values (2) 11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 66448
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 37359
56.2%
D 14447
 
21.7%
E 6076
 
9.1%
F 2362
 
3.6%
C 2229
 
3.4%
G 1951
 
2.9%
B 912
 
1.4%
H 712
 
1.1%
I 363
 
0.5%
K 26
 
< 0.1%
Other values (2) 11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 66448
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 37359
56.2%
D 14447
 
21.7%
E 6076
 
9.1%
F 2362
 
3.6%
C 2229
 
3.4%
G 1951
 
2.9%
B 912
 
1.4%
H 712
 
1.1%
I 363
 
0.5%
K 26
 
< 0.1%
Other values (2) 11
 
< 0.1%

booking_changes
Real number (ℝ)

Zeros 

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.22599627
Minimum0
Maximum20
Zeros56399
Zeros (%)84.9%
Negative0
Negative (%)0.0%
Memory size519.2 KiB
2024-11-12T16:10:10.063813image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum20
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.67243446
Coefficient of variation (CV)2.9754229
Kurtosis71.205598
Mean0.22599627
Median Absolute Deviation (MAD)0
Skewness5.8559966
Sum15017
Variance0.4521681
MonotonicityNot monotonic
2024-11-12T16:10:10.382573image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 56399
84.9%
1 6970
 
10.5%
2 2080
 
3.1%
3 580
 
0.9%
4 236
 
0.4%
5 84
 
0.1%
6 44
 
0.1%
7 21
 
< 0.1%
8 11
 
< 0.1%
9 7
 
< 0.1%
Other values (8) 16
 
< 0.1%
ValueCountFrequency (%)
0 56399
84.9%
1 6970
 
10.5%
2 2080
 
3.1%
3 580
 
0.9%
4 236
 
0.4%
5 84
 
0.1%
6 44
 
0.1%
7 21
 
< 0.1%
8 11
 
< 0.1%
9 7
 
< 0.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
17 2
 
< 0.1%
16 1
 
< 0.1%
15 2
 
< 0.1%
14 1
 
< 0.1%
13 5
< 0.1%
12 1
 
< 0.1%
10 3
 
< 0.1%
9 7
< 0.1%
8 11
< 0.1%

deposit_type
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size519.2 KiB
No Deposit
57290 
Non Refund
9013 
Refundable
 
145

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters664480
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo Deposit
2nd rowNo Deposit
3rd rowNo Deposit
4th rowNo Deposit
5th rowNo Deposit

Common Values

ValueCountFrequency (%)
No Deposit 57290
86.2%
Non Refund 9013
 
13.6%
Refundable 145
 
0.2%

Length

2024-11-12T16:10:10.705236image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-12T16:10:10.984539image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
no 57290
43.2%
deposit 57290
43.2%
non 9013
 
6.8%
refund 9013
 
6.8%
refundable 145
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 123593
18.6%
e 66593
10.0%
N 66303
10.0%
66303
10.0%
s 57290
8.6%
i 57290
8.6%
t 57290
8.6%
p 57290
8.6%
D 57290
8.6%
n 18171
 
2.7%
Other values (7) 37067
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 664480
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 123593
18.6%
e 66593
10.0%
N 66303
10.0%
66303
10.0%
s 57290
8.6%
i 57290
8.6%
t 57290
8.6%
p 57290
8.6%
D 57290
8.6%
n 18171
 
2.7%
Other values (7) 37067
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 664480
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 123593
18.6%
e 66593
10.0%
N 66303
10.0%
66303
10.0%
s 57290
8.6%
i 57290
8.6%
t 57290
8.6%
p 57290
8.6%
D 57290
8.6%
n 18171
 
2.7%
Other values (7) 37067
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 664480
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 123593
18.6%
e 66593
10.0%
N 66303
10.0%
66303
10.0%
s 57290
8.6%
i 57290
8.6%
t 57290
8.6%
p 57290
8.6%
D 57290
8.6%
n 18171
 
2.7%
Other values (7) 37067
 
5.6%

agent
Real number (ℝ)

High correlation  Missing 

Distinct261
Distinct (%)0.5%
Missing10124
Missing (%)15.2%
Infinite0
Infinite (%)0.0%
Mean133.76218
Minimum1
Maximum535
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.2 KiB
2024-11-12T16:10:11.314134image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q19
median134
Q3240
95-th percentile313
Maximum535
Range534
Interquartile range (IQR)231

Descriptive statistics

Standard deviation121.08427
Coefficient of variation (CV)0.9052205
Kurtosis-1.1089051
Mean133.76218
Median Absolute Deviation (MAD)114
Skewness0.29797347
Sum7534021
Variance14661.4
MonotonicityNot monotonic
2024-11-12T16:10:11.696528image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
240 13906
20.9%
9 10785
16.2%
1 3819
 
5.7%
250 2869
 
4.3%
241 1721
 
2.6%
6 1408
 
2.1%
40 1013
 
1.5%
314 927
 
1.4%
242 779
 
1.2%
37 687
 
1.0%
Other values (251) 18410
27.7%
(Missing) 10124
15.2%
ValueCountFrequency (%)
1 3819
 
5.7%
2 125
 
0.2%
3 653
 
1.0%
5 256
 
0.4%
6 1408
 
2.1%
7 633
 
1.0%
8 661
 
1.0%
9 10785
16.2%
10 52
 
0.1%
11 225
 
0.3%
ValueCountFrequency (%)
535 3
 
< 0.1%
531 68
0.1%
527 35
0.1%
526 10
 
< 0.1%
510 2
 
< 0.1%
508 6
 
< 0.1%
502 24
 
< 0.1%
497 1
 
< 0.1%
495 50
0.1%
493 35
0.1%

company
Real number (ℝ)

High correlation  Missing 

Distinct273
Distinct (%)7.3%
Missing62703
Missing (%)94.4%
Infinite0
Infinite (%)0.0%
Mean223.18665
Minimum6
Maximum543
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.2 KiB
2024-11-12T16:10:12.064542image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile38
Q1110
median223
Q3307
95-th percentile484
Maximum543
Range537
Interquartile range (IQR)197

Descriptive statistics

Standard deviation130.33971
Coefficient of variation (CV)0.58399419
Kurtosis-0.51481426
Mean223.18665
Median Absolute Deviation (MAD)101
Skewness0.37818308
Sum835834
Variance16988.439
MonotonicityNot monotonic
2024-11-12T16:10:12.419302image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
223 784
 
1.2%
281 138
 
0.2%
154 133
 
0.2%
67 107
 
0.2%
405 101
 
0.2%
94 87
 
0.1%
47 67
 
0.1%
135 64
 
0.1%
331 59
 
0.1%
498 58
 
0.1%
Other values (263) 2147
 
3.2%
(Missing) 62703
94.4%
ValueCountFrequency (%)
6 1
 
< 0.1%
8 1
 
< 0.1%
9 36
0.1%
10 1
 
< 0.1%
12 14
 
< 0.1%
14 3
 
< 0.1%
16 5
 
< 0.1%
20 50
0.1%
22 6
 
< 0.1%
28 5
 
< 0.1%
ValueCountFrequency (%)
543 2
 
< 0.1%
541 1
 
< 0.1%
539 2
 
< 0.1%
534 2
 
< 0.1%
531 1
 
< 0.1%
530 5
 
< 0.1%
528 2
 
< 0.1%
525 15
< 0.1%
523 19
< 0.1%
521 7
 
< 0.1%

days_in_waiting_list
Real number (ℝ)

Zeros 

Distinct110
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3267818
Minimum0
Maximum391
Zeros63984
Zeros (%)96.3%
Negative0
Negative (%)0.0%
Memory size519.2 KiB
2024-11-12T16:10:12.762657image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum391
Range391
Interquartile range (IQR)0

Descriptive statistics

Standard deviation22.223456
Coefficient of variation (CV)6.6801664
Kurtosis125.59574
Mean3.3267818
Median Absolute Deviation (MAD)0
Skewness9.9470935
Sum221058
Variance493.88202
MonotonicityNot monotonic
2024-11-12T16:10:13.493073image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 63984
96.3%
39 186
 
0.3%
58 164
 
0.2%
31 102
 
0.2%
69 89
 
0.1%
63 80
 
0.1%
87 80
 
0.1%
111 71
 
0.1%
101 65
 
0.1%
77 63
 
0.1%
Other values (100) 1564
 
2.4%
ValueCountFrequency (%)
0 63984
96.3%
1 7
 
< 0.1%
2 3
 
< 0.1%
3 59
 
0.1%
4 11
 
< 0.1%
5 5
 
< 0.1%
6 4
 
< 0.1%
7 1
 
< 0.1%
8 7
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
391 45
0.1%
379 15
 
< 0.1%
330 15
 
< 0.1%
259 10
 
< 0.1%
236 35
0.1%
224 10
 
< 0.1%
223 60
0.1%
215 21
 
< 0.1%
207 15
 
< 0.1%
193 1
 
< 0.1%

customer_type
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size519.2 KiB
Transient
49940 
Transient-Party
13669 
Contract
 
2521
Group
 
318

Length

Max length15
Median length9
Mean length10.177176
Min length5

Characters and Unicode

Total characters676253
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTransient
2nd rowTransient
3rd rowTransient
4th rowTransient
5th rowTransient

Common Values

ValueCountFrequency (%)
Transient 49940
75.2%
Transient-Party 13669
 
20.6%
Contract 2521
 
3.8%
Group 318
 
0.5%

Length

2024-11-12T16:10:13.854889image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-12T16:10:14.110191image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
transient 49940
75.2%
transient-party 13669
 
20.6%
contract 2521
 
3.8%
group 318
 
0.5%

Most occurring characters

ValueCountFrequency (%)
n 129739
19.2%
t 82320
12.2%
r 80117
11.8%
a 79799
11.8%
T 63609
9.4%
s 63609
9.4%
i 63609
9.4%
e 63609
9.4%
y 13669
 
2.0%
- 13669
 
2.0%
Other values (7) 22504
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 676253
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 129739
19.2%
t 82320
12.2%
r 80117
11.8%
a 79799
11.8%
T 63609
9.4%
s 63609
9.4%
i 63609
9.4%
e 63609
9.4%
y 13669
 
2.0%
- 13669
 
2.0%
Other values (7) 22504
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 676253
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 129739
19.2%
t 82320
12.2%
r 80117
11.8%
a 79799
11.8%
T 63609
9.4%
s 63609
9.4%
i 63609
9.4%
e 63609
9.4%
y 13669
 
2.0%
- 13669
 
2.0%
Other values (7) 22504
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 676253
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 129739
19.2%
t 82320
12.2%
r 80117
11.8%
a 79799
11.8%
T 63609
9.4%
s 63609
9.4%
i 63609
9.4%
e 63609
9.4%
y 13669
 
2.0%
- 13669
 
2.0%
Other values (7) 22504
 
3.3%

adr
Real number (ℝ)

Zeros 

Distinct7051
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.321678
Minimum-6.38
Maximum5400
Zeros975
Zeros (%)1.5%
Negative1
Negative (%)< 0.1%
Memory size519.2 KiB
2024-11-12T16:10:14.408206image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-6.38
5-th percentile35
Q162
median85
Q3120
95-th percentile202.8625
Maximum5400
Range5406.38
Interquartile range (IQR)58

Descriptive statistics

Standard deviation56.126409
Coefficient of variation (CV)0.58269759
Kurtosis1201.0133
Mean96.321678
Median Absolute Deviation (MAD)26.6
Skewness13.715711
Sum6400382.9
Variance3150.1738
MonotonicityNot monotonic
2024-11-12T16:10:15.319998image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 2042
 
3.1%
75 1377
 
2.1%
65 1124
 
1.7%
48 1036
 
1.6%
80 1032
 
1.6%
0 975
 
1.5%
90 950
 
1.4%
60 899
 
1.4%
100 880
 
1.3%
85 853
 
1.3%
Other values (7041) 55280
83.2%
ValueCountFrequency (%)
-6.38 1
 
< 0.1%
0 975
1.5%
0.26 1
 
< 0.1%
0.5 1
 
< 0.1%
1 3
 
< 0.1%
1.48 1
 
< 0.1%
1.56 2
 
< 0.1%
1.8 1
 
< 0.1%
2 8
 
< 0.1%
2.4 1
 
< 0.1%
ValueCountFrequency (%)
5400 1
< 0.1%
508 1
< 0.1%
450 1
< 0.1%
437 1
< 0.1%
426.25 1
< 0.1%
402 1
< 0.1%
397.38 1
< 0.1%
392 2
< 0.1%
388 2
< 0.1%
387 1
< 0.1%

required_car_parking_spaces
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size519.2 KiB
0
60795 
1
 
5625
2
 
25
8
 
2
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters66448
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 60795
91.5%
1 5625
 
8.5%
2 25
 
< 0.1%
8 2
 
< 0.1%
3 1
 
< 0.1%

Length

2024-11-12T16:10:15.623081image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-12T16:10:15.918105image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 60795
91.5%
1 5625
 
8.5%
2 25
 
< 0.1%
8 2
 
< 0.1%
3 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 60795
91.5%
1 5625
 
8.5%
2 25
 
< 0.1%
8 2
 
< 0.1%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 66448
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 60795
91.5%
1 5625
 
8.5%
2 25
 
< 0.1%
8 2
 
< 0.1%
3 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 66448
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 60795
91.5%
1 5625
 
8.5%
2 25
 
< 0.1%
8 2
 
< 0.1%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 66448
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 60795
91.5%
1 5625
 
8.5%
2 25
 
< 0.1%
8 2
 
< 0.1%
3 1
 
< 0.1%

total_of_special_requests
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49870575
Minimum0
Maximum5
Zeros42616
Zeros (%)64.1%
Negative0
Negative (%)0.0%
Memory size519.2 KiB
2024-11-12T16:10:16.215460image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.76302471
Coefficient of variation (CV)1.5300098
Kurtosis1.9648939
Mean0.49870575
Median Absolute Deviation (MAD)0
Skewness1.5218721
Sum33138
Variance0.5822067
MonotonicityNot monotonic
2024-11-12T16:10:16.465994image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 42616
64.1%
1 16055
 
24.2%
2 6436
 
9.7%
3 1167
 
1.8%
4 160
 
0.2%
5 14
 
< 0.1%
ValueCountFrequency (%)
0 42616
64.1%
1 16055
 
24.2%
2 6436
 
9.7%
3 1167
 
1.8%
4 160
 
0.2%
5 14
 
< 0.1%
ValueCountFrequency (%)
5 14
 
< 0.1%
4 160
 
0.2%
3 1167
 
1.8%
2 6436
 
9.7%
1 16055
 
24.2%
0 42616
64.1%

reservation_status
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size519.2 KiB
Check-Out
34681 
Canceled
30775 
No-Show
 
992

Length

Max length9
Median length9
Mean length8.506998
Min length7

Characters and Unicode

Total characters565273
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCheck-Out
2nd rowCheck-Out
3rd rowCheck-Out
4th rowCheck-Out
5th rowCheck-Out

Common Values

ValueCountFrequency (%)
Check-Out 34681
52.2%
Canceled 30775
46.3%
No-Show 992
 
1.5%

Length

2024-11-12T16:10:16.788206image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-12T16:10:17.066687image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
check-out 34681
52.2%
canceled 30775
46.3%
no-show 992
 
1.5%

Most occurring characters

ValueCountFrequency (%)
e 96231
17.0%
C 65456
11.6%
c 65456
11.6%
h 35673
 
6.3%
- 35673
 
6.3%
u 34681
 
6.1%
t 34681
 
6.1%
O 34681
 
6.1%
k 34681
 
6.1%
a 30775
 
5.4%
Other values (7) 97285
17.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 565273
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 96231
17.0%
C 65456
11.6%
c 65456
11.6%
h 35673
 
6.3%
- 35673
 
6.3%
u 34681
 
6.1%
t 34681
 
6.1%
O 34681
 
6.1%
k 34681
 
6.1%
a 30775
 
5.4%
Other values (7) 97285
17.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 565273
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 96231
17.0%
C 65456
11.6%
c 65456
11.6%
h 35673
 
6.3%
- 35673
 
6.3%
u 34681
 
6.1%
t 34681
 
6.1%
O 34681
 
6.1%
k 34681
 
6.1%
a 30775
 
5.4%
Other values (7) 97285
17.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 565273
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 96231
17.0%
C 65456
11.6%
c 65456
11.6%
h 35673
 
6.3%
- 35673
 
6.3%
u 34681
 
6.1%
t 34681
 
6.1%
O 34681
 
6.1%
k 34681
 
6.1%
a 30775
 
5.4%
Other values (7) 97285
17.2%
Distinct921
Distinct (%)1.4%
Missing1
Missing (%)< 0.1%
Memory size519.2 KiB
Minimum2014-11-18 00:00:00
Maximum2017-09-14 00:00:00
2024-11-12T16:10:17.378129image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:10:17.736974image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2024-11-12T16:09:47.732199image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:08:52.091616image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:08:56.572430image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:00.525258image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:04.362301image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:08.032366image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:13.030456image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:17.044455image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:23.318249image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:27.899483image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:31.728871image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:35.540133image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:40.489477image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:44.243206image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:48.008057image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:08:52.392180image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:08:56.936016image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:00.796942image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:04.624535image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:08.349617image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:13.335382image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:17.450568image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:23.970129image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:28.183784image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:31.982288image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:35.780596image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:40.779123image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:44.509153image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:48.254099image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:08:52.634509image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:08:57.305310image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:01.052760image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:04.884973image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:08.693072image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:13.622313image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:17.946860image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:24.340918image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:28.469483image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:32.238234image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:36.039829image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:41.045614image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:44.756595image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:48.518236image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:08:52.879597image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:08:57.659445image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:01.518450image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:05.148891image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:09.071582image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:13.909130image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:18.215245image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:24.669443image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:28.741423image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:32.508518image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:36.433667image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:41.323151image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:45.016315image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:48.787585image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:08:53.141395image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:08:58.061201image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:01.787638image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:05.420371image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:09.695218image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:14.215953image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:18.770926image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:25.034014image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:29.023901image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:32.890618image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:37.153600image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:41.589787image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:45.266682image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:49.038366image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:08:53.407130image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:08:58.300101image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:02.038131image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:05.668914image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:10.056677image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:14.531293image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:19.042142image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:25.454448image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:29.300042image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:33.157357image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:37.521966image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:41.858252image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:45.508024image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:49.275566image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:08:53.652912image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:08:58.531230image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:02.290642image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:05.936608image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:10.442240image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:14.778330image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:19.883620image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:25.784863image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:29.566786image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:33.430596image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:37.889138image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:42.119141image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:45.740723image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:49.528098image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:08:53.904009image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:08:58.803101image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:02.543700image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:06.221716image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:10.795734image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:15.035172image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:20.381373image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:26.104937image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:29.827497image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:33.681080image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:38.253096image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:42.384377image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:45.985062image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:49.787632image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:08:54.321843image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:08:59.041638image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:02.826687image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:06.496394image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:11.198214image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:15.341457image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:20.701007image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:26.369665image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:30.081299image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:33.930248image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:38.615255image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:42.650865image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:46.223361image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:50.152225image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:08:54.736388image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:08:59.310747image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:03.089547image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:06.763027image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:11.614720image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:15.640135image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:20.981301image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:26.631169image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:30.363780image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:34.197488image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:38.977348image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:42.940646image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:46.485018image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:50.464330image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:08:55.107600image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:08:59.536984image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:03.334912image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:07.008575image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:12.007968image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:15.885399image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:21.237153image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:26.862079image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:30.607281image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:34.503495image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:39.338091image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:43.194137image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:46.714455image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:50.784155image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:08:55.501258image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:08:59.787142image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:03.592732image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:07.250576image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:12.261607image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:16.149096image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:21.550065image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:27.119609image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:30.883066image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:34.761361image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:39.679326image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:43.454995image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:46.964362image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:51.193278image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:08:55.864442image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:00.041984image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:03.869160image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:07.518975image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:12.530503image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:16.469158image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:21.853092image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:27.392831image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:31.174284image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:35.024825image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:39.975321image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:43.742197image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:47.230966image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:51.545329image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:08:56.216339image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:00.281176image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:04.107157image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:07.763224image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:12.764090image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:16.754043image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:22.219470image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:27.644934image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:31.460530image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:35.291407image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:40.201052image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:43.994853image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-12T16:09:47.492589image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-11-12T16:10:18.121659image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
adradultsagentarrival_date_day_of_montharrival_date_montharrival_date_week_numberarrival_date_yearassigned_room_typebabiesbooking_changeschildrencompanycustomer_typedays_in_waiting_listdeposit_typedistribution_channelhotelis_canceledis_repeated_guestlead_timemarket_segmentmealprevious_bookings_not_canceledprevious_cancellationsrequired_car_parking_spacesreservation_statusreserved_room_typestays_in_week_nightsstays_in_weekend_nightstotal_of_special_requests
adr1.0000.307-0.0010.0250.0000.0980.0000.0000.000-0.0110.000-0.0140.0000.0160.0080.0000.0000.0000.0000.0770.0000.000-0.143-0.0820.0000.0000.0000.1420.0960.131
adults0.3071.000-0.0160.0140.0110.0390.0170.0000.000-0.0450.0000.2100.121-0.0320.0000.0100.0080.0120.0000.1740.0100.000-0.206-0.0240.0000.0070.0000.1490.1320.135
agent-0.001-0.0161.0000.0090.116-0.0730.1910.1450.0320.1470.0740.5250.176-0.1020.2060.1990.8210.3450.077-0.1190.2830.2180.0690.0430.1460.2460.1530.1570.1520.215
arrival_date_day_of_month0.0250.0140.0091.0000.0670.0710.0570.0140.0040.0090.0110.0820.0330.0330.0750.0390.0440.0300.014-0.0080.0440.0470.007-0.0210.0120.0300.014-0.020-0.0040.011
arrival_date_month0.0000.0110.1160.0671.0000.7980.4400.0410.0280.0120.0750.2940.1210.0820.1300.0750.1510.0860.0960.1360.1060.1090.0170.0460.0230.0820.0610.0510.0680.071
arrival_date_week_number0.0980.039-0.0730.0710.7981.0000.4470.0440.0250.0060.0670.0170.1210.0290.1220.0710.1450.0800.0990.1260.0980.100-0.0580.0450.0200.0760.0570.0330.0250.024
arrival_date_year0.0000.0170.1910.0570.4400.4471.0000.0750.0070.0220.0470.3180.1680.0870.0710.0360.1820.2040.0760.1410.1210.1050.0360.0590.0170.1440.1060.0330.0540.089
assigned_room_type0.0000.0000.1450.0140.0410.0440.0751.0000.0570.0750.3210.0880.0840.0380.2240.1090.4030.2950.0870.0630.1410.1070.0120.0160.1010.2100.7840.0530.0780.085
babies0.0000.0000.0320.0040.0280.0250.0070.0571.0000.0200.0330.0480.0130.0000.0270.0270.0550.0470.0100.0000.0370.0200.0000.0000.0270.0330.0520.0000.0170.094
booking_changes-0.011-0.0450.1470.0090.0120.0060.0220.0750.0201.0000.0210.1360.035-0.0170.0320.0290.0450.0620.0000.0160.0220.0140.028-0.0240.0200.0430.0150.0970.0660.064
children0.0000.0000.0740.0110.0750.0670.0470.3210.0330.0211.0000.0380.0570.0210.0790.0410.0550.0340.0300.0280.1050.0330.0000.0000.0300.0320.3790.0100.0350.050
company-0.0140.2100.5250.0820.2940.0170.3180.0880.0480.1360.0381.0000.299-0.0070.2370.2660.4370.2320.1810.1860.3480.229-0.074-0.0430.0660.1650.1020.1290.1110.087
customer_type0.0000.1210.1760.0330.1210.1210.1680.0840.0130.0350.0570.2991.0000.1050.1520.0910.0650.2340.1480.0710.2940.1340.0330.0050.0460.1660.1080.1070.1260.106
days_in_waiting_list0.016-0.032-0.1020.0330.0820.0290.0870.0380.000-0.0170.021-0.0070.1051.0000.1210.0310.1770.0500.0240.1700.0890.064-0.032-0.0250.0390.0390.034-0.001-0.091-0.128
deposit_type0.0080.0000.2060.0750.1300.1220.0710.2240.0270.0320.0790.2370.1520.1211.0000.0960.3350.4080.0650.2910.3690.0840.0210.0630.0880.2970.1730.0640.1000.209
distribution_channel0.0000.0100.1990.0390.0750.0710.0360.1090.0270.0290.0410.2660.0910.0310.0961.0000.2550.2270.2130.1130.6700.0680.1090.0360.0830.1640.1170.0110.0660.073
hotel0.0000.0080.8210.0440.1510.1450.1820.4030.0550.0450.0550.4370.0650.1770.3350.2551.0000.4940.1340.1650.2340.3180.0670.0380.2290.4950.3230.1500.1820.214
is_canceled0.0000.0120.3450.0300.0860.0800.2040.2950.0470.0620.0340.2320.2340.0500.4080.2270.4941.0000.1380.2290.2560.1860.0740.0470.2921.0000.1200.0680.0540.212
is_repeated_guest0.0000.0000.0770.0140.0960.0990.0760.0870.0100.0000.0300.1810.1480.0240.0650.2130.1340.1381.0000.1200.2640.0530.3150.0670.0850.1380.0300.0190.0770.064
lead_time0.0770.174-0.119-0.0080.1360.1260.1410.0630.0000.0160.0280.1860.0710.1700.2910.1130.1650.2290.1201.0000.1770.086-0.1880.0750.0690.1720.0480.3580.209-0.082
market_segment0.0000.0100.2830.0440.1060.0980.1210.1410.0370.0220.1050.3480.2940.0890.3690.6700.2340.2560.2640.1771.0000.1770.0930.0380.1060.1900.1540.0470.0790.204
meal0.0000.0000.2180.0470.1090.1000.1050.1070.0200.0140.0330.2290.1340.0640.0840.0680.3180.1860.0530.0860.1771.0000.0160.0880.0310.1350.0900.0480.0770.045
previous_bookings_not_canceled-0.143-0.2060.0690.0070.017-0.0580.0360.0120.0000.0280.000-0.0740.033-0.0320.0210.1090.0670.0740.315-0.1880.0930.0161.0000.1250.0260.0520.006-0.114-0.0900.025
previous_cancellations-0.082-0.0240.043-0.0210.0460.0450.0590.0160.000-0.0240.000-0.0430.005-0.0250.0630.0360.0380.0470.0670.0750.0380.0880.1251.0000.0000.0340.0110.0080.007-0.031
required_car_parking_spaces0.0000.0000.1460.0120.0230.0200.0170.1010.0270.0200.0300.0660.0460.0390.0880.0830.2290.2920.0850.0690.1060.0310.0260.0001.0000.2060.0830.0220.0210.060
reservation_status0.0000.0070.2460.0300.0820.0760.1440.2100.0330.0430.0320.1650.1660.0390.2970.1640.4951.0000.1380.1720.1900.1350.0520.0340.2061.0000.0850.0520.0410.152
reserved_room_type0.0000.0000.1530.0140.0610.0570.1060.7840.0520.0150.3790.1020.1080.0340.1730.1170.3230.1200.0300.0480.1540.0900.0060.0110.0830.0851.0000.0470.0640.089
stays_in_week_nights0.1420.1490.157-0.0200.0510.0330.0330.0530.0000.0970.0100.1290.107-0.0010.0640.0110.1500.0680.0190.3580.0470.048-0.1140.0080.0220.0520.0471.0000.3900.105
stays_in_weekend_nights0.0960.1320.152-0.0040.0680.0250.0540.0780.0170.0660.0350.1110.126-0.0910.1000.0660.1820.0540.0770.2090.0790.077-0.0900.0070.0210.0410.0640.3901.0000.110
total_of_special_requests0.1310.1350.2150.0110.0710.0240.0890.0850.0940.0640.0500.0870.106-0.1280.2090.0730.2140.2120.064-0.0820.2040.0450.025-0.0310.0600.1520.0890.1050.1101.000

Missing values

2024-11-12T16:09:52.290658image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-12T16:09:54.389349image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-11-12T16:09:55.397018image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typeagentcompanydays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_date
0Resort Hotel03422015July2710020.00BBPRTDirectDirect000CC3No DepositNaNNaN0Transient0.000Check-Out2015-07-01
1Resort Hotel07372015July2710020.00BBPRTDirectDirect000CC4No DepositNaNNaN0Transient0.000Check-Out2015-07-01
2Resort Hotel072015July2710110.00BBGBRDirectDirect000AC0No DepositNaNNaN0Transient75.000Check-Out2015-07-02
3Resort Hotel0132015July2710110.00BBGBRCorporateCorporate000AA0No Deposit304.0NaN0Transient75.000Check-Out2015-07-02
4Resort Hotel0142015July2710220.00BBGBROnline TATA/TO000AA0No Deposit240.0NaN0Transient98.001Check-Out2015-07-03
5Resort Hotel0142015July2710220.00BBGBROnline TATA/TO000AA0No Deposit240.0NaN0Transient98.001Check-Out2015-07-03
6Resort Hotel002015July2710220.00BBPRTDirectDirect000CC0No DepositNaNNaN0Transient107.000Check-Out2015-07-03
7Resort Hotel092015July2710220.00FBPRTDirectDirect000CC0No Deposit303.0NaN0Transient103.001Check-Out2015-07-03
8Resort Hotel1852015July2710320.00BBPRTOnline TATA/TO000AA0No Deposit240.0NaN0Transient82.001Canceled2015-05-06
9Resort Hotel1752015July2710320.00HBPRTOffline TA/TOTA/TO000DD0No Deposit15.0NaN0Transient105.500Canceled2015-04-22
hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typeagentcompanydays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_date
66438City Hotel1982017April16200320.00BBGBROnline TATA/TO000DD0No Deposit9.0NaN0Transient136.802Canceled2017-01-29
66439City Hotel1672017April16200320.00BBISROnline TATA/TO000DD0No Deposit9.0NaN0Transient135.000Canceled2017-02-16
66440City Hotel11682017April16200330.00BBBRAOnline TATA/TO000DD0No Deposit9.0NaN0Transient166.503Canceled2017-03-06
66441City Hotel11252017April16200320.00BBPRTGroupsTA/TO000AA0Non Refund33.0NaN0Transient85.000Canceled2016-12-16
66442City Hotel11252017April16200320.00BBPRTGroupsTA/TO000AA0Non Refund33.0NaN0Transient85.000Canceled2016-12-16
66443City Hotel11252017April16200320.00BBPRTGroupsTA/TO000AA0Non Refund33.0NaN0Transient85.000Canceled2016-12-16
66444City Hotel11252017April16200320.00BBPRTGroupsTA/TO000AA0Non Refund33.0NaN0Transient85.000Canceled2016-12-16
66445City Hotel11432017April16200320.00BBPRTGroupsTA/TO000AA0Non Refund33.0NaN18Transient85.000Canceled2016-12-16
66446City Hotel11252017April16200320.00BBPRTGroupsTA/TO000AA0Non Refund33.0NaN0Transient85.000Canceled2016-12-16
66447City Hotel11252017April16200320.00BBPRTGroupsTA/TO000AA0Non Refund33.0NaN0Transient85.000CanceledNaN

Duplicate rows

Most frequently occurring

hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typeagentcompanydays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_date# duplicates
1871City Hotel12772016November4671220.00BBPRTGroupsTA/TO000AA0Non RefundNaNNaN0Transient100.000Canceled2016-04-04180
1691City Hotel11882016June25150210.00BBPRTOffline TA/TOTA/TO000AA0Non Refund119.0NaN39Transient130.000Canceled2016-01-18109
1585City Hotel11582016May22240210.00BBPRTGroupsTA/TO000AA0Non Refund37.0NaN31Transient130.000Canceled2016-01-18101
815City Hotel1282017March920320.00BBPRTGroupsTA/TO000AA0Non RefundNaNNaN0Transient95.000Canceled2017-02-0299
903City Hotel1382017January2140110.00BBPRTCorporateCorporate000AA0Non RefundNaN67.00Transient75.000Canceled2016-12-0799
1138City Hotel1712016June25140310.00BBPRTOffline TA/TOTA/TO000AA0Non Refund236.0NaN0Transient120.000Canceled2016-04-2789
1620City Hotel11662016November4510310.00BBPRTOffline TA/TOTA/TO000AA0Non Refund236.0NaN0Transient110.000Canceled2016-07-1385
1919City Hotel13042016November4530320.00BBPRTOffline TA/TOTA/TO000AA0Non Refund21.0NaN0Transient89.000Canceled2016-02-0185
1920City Hotel13052016November4541220.00BBPRTOffline TA/TOTA/TO000AA0Non Refund21.0NaN0Transient89.000Canceled2016-02-0185
892City Hotel1372016October42130320.00BBPRTOffline TA/TOTA/TO000AA0No Deposit56.0NaN0Transient-Party105.000Canceled2016-09-0684